With the development of three-dimensional (3D) modeling technologies and abundance of the model resources, personalized 3D model editing has attracted more and more attention, how to speed up the degree of automation of the 3D model editing procedure is one of the research hotspots. However, due to the huge differences between different 3D model structures and the disunity of the model sizes, automation of 3D model editing turns to be a difficult problem.
In order to resolve the problem, a 3D model library is introduced as a guide. By compiling statistics of the design knowledge priors of the models in the 3D model library, the degree of automation of the 3D model editing procedure can be improved. There are plenty of literatures about 3D model editing in the field of computer graphics, without enumerating all the methods, reconstructing and editing of 3D models and related works will be the focus, as well as the combination constructing of the 3D models.
According to different sources of guiding information, the 3D model editing methods can be categorized into three types: guiding the 3D model editing procedure using the learned design knowledge prior of the model library; reducing the complexity of the 3D model editing procedure by using two-dimensional (2D) model information to guide the deformation of 3D models; a 3D model editing method based on analogy, which constructs a procedure tree referring to the variations during the procedure, and applies the procedure tree self-adaptively to the input model. In 2012, Mehmet Ersin Yumer et al from Carnegie Mellon University proposed a method which uses model library priors to edit 3D models. In 2010, Kai Xu et al from Simon Fraser University proposed a method of model style migration based on mutual analysis, where the method obtains the correlations between model components by analyzing the geometrical characteristics of the components in the 3D model library. In 2010, Alee Rivers et al from Massachusetts Institute of Technology proposed a method for 3D modeling based on 2D contour line, which uses the analytical result of the line rule to guide the deformation and combination constructing of the 3D model elements. In 2011, Kai Xu et al from National University of Defense Technology proposed a method for editing and generating 3D modeling guided by 2D images; by analyzing the design rule of 2D images, the rule is migrated to 3D models and 3D models that fit the image style are generated. In 2004, Robert W. Summe et al from Massachusetts Institute of Technology proposed a method of deformation migrating of 3D models based on 3D meshes, where the deformation migrating procedure based on meshes includes three steps: first, a user is needed to assign a corresponding relationship between the triangular patch of source model and the target model, then migrating the variation of the triangular patch of the source model directly to the target model, finally, adjusting the positions of other patches by solving constrained optimization. In 2014, Chongyang Ma et al from Columbia University proposed model deformation based on analogy. The method obtains the analogy relationship between the source model and the target model and applies the analogy relationship to an example model, and then obtains the deformation migrated 3D model.
There are plenty of researches about the reconstructing and editing methods of 3D models. In 2004, Thomas Funkhouse et al from Princeton University published the first paper about using high level information for 3D modeling, the method uses existing library models to combine and generate new 3D models. In 2011, Siddhartha Chaudhuri et al from Stanford University proposed a model combination method based on bayesian network learning. In 2011, Kai Xu et al from National University of Defense Technology proposed a 3D modeling method guided by images; by analyzing the design rule of 2D image models, the rule is migrated to 3D models and 3D model results that fit the image style are generated. In 2013, Youyi Zheng et al from King Abdullah University of Science and Technology proposed a new model combination based on functions. The method inputs two or more segmented models, and obtains a large amount of 3D models by substituting the symmetric functional sub-structure of respective models.